Exploring the Landscape of Distributed Graph Sketching
David Tench, Evan T. West, Kenny Zhang, Michael Bender, Daniel DeLayo,, Martin Farach-Colton, Gilvir Gill, Tyler Seip, Victor Zhang

TL;DR
This paper introduces Landscape, a distributed graph-stream processing system that leverages linear sketching to overcome CPU and network bottlenecks, enabling high-speed graph updates and scalable processing with minimal communication overhead.
Contribution
The paper presents Landscape, a novel distributed system utilizing linear sketching to efficiently process dense graph streams without worker communication, surpassing existing performance limits.
Findings
Landscape achieves 332 million updates/sec on a 2^17 vertex graph.
System scales 35x faster with 40 workers compared to 1 worker.
Query latency reduced by up to four orders of magnitude.
Abstract
Recent work has initiated the study of dense graph processing using graph sketching methods, which drastically reduce space costs by lossily compressing information about the input graph. In this paper, we explore the strange and surprising performance landscape of sketching algorithms. We highlight both their surprising advantages for processing dense graphs that were previously prohibitively expensive to study, as well as the current limitations of the technique. Most notably, we show how sketching can avoid bottlenecks that limit conventional graph processing methods. Single-machine streaming graph processing systems are typically bottlenecked by CPU performance, and distributed graph processing systems are typically bottlenecked by network latency. We present Landscape, a distributed graph-stream processing system that uses linear sketching to distribute the CPU work of computing…
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Taxonomy
TopicsGraph Theory and Algorithms · Semantic Web and Ontologies · Data Mining Algorithms and Applications
